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Posted to issues@spark.apache.org by "Reynold Xin (JIRA)" <ji...@apache.org> on 2015/06/03 10:12:49 UTC

[jira] [Created] (SPARK-8072) Better AnalysisException for writing DataFrame with identically named columns

Reynold Xin created SPARK-8072:
----------------------------------

             Summary: Better AnalysisException for writing DataFrame with identically named columns
                 Key: SPARK-8072
                 URL: https://issues.apache.org/jira/browse/SPARK-8072
             Project: Spark
          Issue Type: Sub-task
          Components: SQL
            Reporter: Reynold Xin
            Priority: Blocker


We should check if there are duplicate columns, and if yes, throw an explicit error message saying there are duplicate columns. See current error message below. 

{code}
In [3]: df.withColumn('age', df.age)
Out[3]: DataFrame[age: bigint, name: string, age: bigint]

In [4]: df.withColumn('age', df.age).write.parquet('test-parquet.out')
---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-4-eecb85256898> in <module>()
----> 1 df.withColumn('age', df.age).write.parquet('test-parquet.out')

/scratch/rxin/spark/python/pyspark/sql/readwriter.py in parquet(self, path, mode)
    350         >>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
    351         """
--> 352         self._jwrite.mode(mode).parquet(path)
    353 
    354     @since(1.4)

/Users/rxin/anaconda/lib/python2.7/site-packages/py4j-0.8.1-py2.7.egg/py4j/java_gateway.pyc in __call__(self, *args)
    535         answer = self.gateway_client.send_command(command)
    536         return_value = get_return_value(answer, self.gateway_client,
--> 537                 self.target_id, self.name)
    538 
    539         for temp_arg in temp_args:

/Users/rxin/anaconda/lib/python2.7/site-packages/py4j-0.8.1-py2.7.egg/py4j/protocol.pyc in get_return_value(answer, gateway_client, target_id, name)
    298                 raise Py4JJavaError(
    299                     'An error occurred while calling {0}{1}{2}.\n'.
--> 300                     format(target_id, '.', name), value)
    301             else:
    302                 raise Py4JError(

Py4JJavaError: An error occurred while calling o35.parquet.
: org.apache.spark.sql.AnalysisException: Reference 'age' is ambiguous, could be: age#0L, age#3L.;
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:279)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveChildren(LogicalPlan.scala:116)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8$$anonfun$applyOrElse$4$$anonfun$16.apply(Analyzer.scala:350)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8$$anonfun$applyOrElse$4$$anonfun$16.apply(Analyzer.scala:350)
	at org.apache.spark.sql.catalyst.analysis.package$.withPosition(package.scala:48)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8$$anonfun$applyOrElse$4.applyOrElse(Analyzer.scala:350)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8$$anonfun$applyOrElse$4.applyOrElse(Analyzer.scala:341)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:285)
	at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$transformExpressionUp$1(QueryPlan.scala:108)
	at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2$$anonfun$apply$2.apply(QueryPlan.scala:123)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
	at scala.collection.immutable.List.foreach(List.scala:318)
	at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
	at scala.collection.AbstractTraversable.map(Traversable.scala:105)
	at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:122)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
	at scala.collection.AbstractIterator.to(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
	at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
	at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
	at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:127)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8.applyOrElse(Analyzer.scala:341)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8.applyOrElse(Analyzer.scala:243)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
	at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:285)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:243)
	at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:242)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:61)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:59)
	at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
	at scala.collection.immutable.List.foldLeft(List.scala:84)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:59)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:51)
	at scala.collection.immutable.List.foreach(List.scala:318)
	at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:51)
	at org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:903)
	at org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:903)
	at org.apache.spark.sql.SQLContext$QueryExecution.assertAnalyzed(SQLContext.scala:901)
	at org.apache.spark.sql.DataFrame.<init>(DataFrame.scala:131)
	at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:51)
	at org.apache.spark.sql.sources.InsertIntoHadoopFsRelation.run(commands.scala:98)
	at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
	at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
	at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:68)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:88)
	at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:88)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:148)
	at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:87)
	at org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:920)
	at org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:920)
	at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:338)
	at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:144)
	at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:135)
	at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:281)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:606)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
	at py4j.Gateway.invoke(Gateway.java:259)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:207)
	at java.lang.Thread.run(Thread.java:744)
{code}



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